An assessment of the surface climate in the NCEP climate forecast system reanalysis

Wang, Wanqiu; Xie, Pingping; Yoo, Seunghwa; Xue, Yan; Kumar, Arun; Wu, Xuan

doi:10.1007/s00382-010-0935-7

Cited by 154 publications

(117 citation statements)

References 44 publications

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“…The CFSR reanalysis clearly assigns the highest precipitation amounts to the Amazon, Mekong and Mackenzie river basins, particularly during the wet seasons. This over-estimation, also found by Wang et al (2010), may result from a bias in the updated reanalysis states (Zhang et al, 2012). The differences between the three meteorological datasets appear smallest for the Rhine (with a maximum difference of 30 mm/month) and Murray watersheds (maximum difference of 18 mm/month).…”

Section: Comparison Of Meteorological Forcing Datasetsmentioning

confidence: 75%

Significant uncertainty in global scale hydrological modeling from precipitation data errors

Weiland

Vrugt

Beek

et al. 2015

Journal of Hydrology

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Keywords:Global hydrological modeling Forcing uncertainty Model calibration Parameter uncertainty s u m m a r yIn the past decades significant progress has been made in the fitting of hydrologic models to data. Most of this work has focused on simple, CPU-efficient, lumped hydrologic models using discharge, water table depth, soil moisture, or tracer data from relatively small river basins. In this paper, we focus on largescale hydrologic modeling and analyze the effect of parameter and rainfall data uncertainty on simulated discharge dynamics with the global hydrologic model PCR-GLOBWB. We use three rainfall data products; the CFSR reanalysis, the ERA-Interim reanalysis, and a combined ERA-40 reanalysis and CRU dataset. Parameter uncertainty is derived from Latin Hypercube Sampling (LHS) using monthly discharge data from five of the largest river systems in the world. Our results demonstrate that the default parameterization of PCR-GLOBWB, derived from global datasets, can be improved by calibrating the model against monthly discharge observations. Yet, it is difficult to find a single parameterization of PCR-GLOBWB that works well for all of the five river basins considered herein and shows consistent performance during both the calibration and evaluation period. Still there may be possibilities for regionalization based on catchment similarities. Our simulations illustrate that parameter uncertainty constitutes only a minor part of predictive uncertainty. Thus, the apparent dichotomy between simulations of global-scale hydrologic behavior and actual data cannot be resolved by simply increasing the model complexity of PCR-GLOBWB and resolving sub-grid processes. Instead, it would be more productive to improve the characterization of global rainfall amounts at spatial resolutions of 0.5°and smaller.

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Section: Comparison Of Meteorological Forcing Datasetsmentioning

confidence: 75%

Significant uncertainty in global scale hydrological modeling from precipitation data errors

Weiland

Vrugt

Beek

et al. 2015

Journal of Hydrology

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“…The CFSv2, on the other hand, uses prescribed CO 2 concentrations as a function of time in its atmospheric initial condition (Cai et al 2009). The large warming trend in the eastern Pacific SST is primarily associated with changes in satellite observing system that occurred in 1998/1999 period that were assimilated in the CFSR Wang et al 2011). An assessment of the trend is beyond the scope of this study, but it certainly needs further examination.…”

Section: Enso Predictionmentioning

confidence: 99%

Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter

2012

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The seasonal prediction skill for the Northern Hemisphere winter is assessed using retrospective predictions from the ECMWF System 4 (Sys4) and National Center for Environmental Prediction (NCEP) CFS version 2 (CFSv2) coupled atmosphere-ocean seasonal climate prediction systems. Sys4 shows a cold bias in the equatorial Pacific but a warm bias is found in the North Pacific and part of the North Atlantic. The CFSv2 has strong warm bias from the cold tongue region of the eastern Pacific to the equatorial central Pacific and cold bias in broad areas over the North Pacific and the North Atlantic. A cold bias in the Southern Hemisphere is common in both reforecasts. In addition, excessive precipitation is found in the equatorial Pacific, the equatorial Indian Ocean and the western Pacific in Sys4, and in the South Pacific, the southern Indian Ocean and the western Pacific in CFSv2. A dry bias is found for both modeling systems over South America and northern Australia. The mean prediction skill of 2 meter temperature (2mT) and precipitation anomalies are greater over the tropics than the extra-tropics and also greater over ocean than land. The prediction skill of tropical 2mT and precipitation is greater in strong El Nino Southern Oscillation (ENSO) winters than in weak ENSO winters. Both models predict the year-to-year ENSO variation quite accurately, although sea surface temperature trend bias in CFSv2 over the tropical Pacific results in lower prediction skill for the CFSv2 relative to the Sys4. Both models capture the main ENSO teleconnection pattern of strong anomalies over the tropics, the North Pacific and the North America. However, both models have difficulty in forecasting the year-to-year winter temperature variability over the US and northern Europe.

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“…[23] Due to the changes in satellite observations in 1999 that are assimilated in the CFSR Wang et al, 2011], the CFSv2 prediction skills for individual variables are also examined using two sets of climatologies (1982-1998 and 1999-2012). Results show that the CFSv2 provides slightly lower skill for predictor NATLSST and HGT500, and slightly higher skill for WPVZWS and U850 (not shown), suggesting that using the 1982-2012 climatology should not limit the hybrid system capability.…”

Section: Cfsv2 Prediction Skillsmentioning

confidence: 99%

A dynamical‐statistical forecast model for the annual frequency of western Pacific tropical cyclones based on the NCEP Climate Forecast System version 2

Yang

Wang

et al. 2013

JGR Atmospheres

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A dynamical‐statistical forecast model for the annual tropical cyclones over the western North Pacific is developed based on the empirical relationship between the actual annual number of tropical cyclones (ANTCs) and the dynamical predictions of large‐scale variables by the Climate Forecast System version 2 of the National Centers for Environmental Prediction (NCEP). On interannual time scales, the ANTCs are significantly and negatively correlated with the July–October tropical North Atlantic sea surface temperature, tropical western Pacific vertical zonal wind shear (WPVZWS), and subtropical Pacific geopotential height at 500 hPa (HGT500). They are also positively correlated with the zonal wind at 850 hPa over the tropical Pacific Ocean. Skillful forecasts of the above four potential predictors are made with the 24‐member ensemble predictions by the NCEP model. The two‐predictor model with the HGT500 and the WPVZWS shows the most skillful hindcasts at 0–2 month leads assessed by the leave‐one‐out cross validation for the ANTCs over the 31 year record between 1982 and 2012. The corresponding correlation coefficients and the root‐mean‐square errors (RMSEs) between the observed and hindcast ANTCs are in the ranges from 0.73 to 0.79 and from 3.11 to 2.75, respectively. Observed ANTCs during El Niño–Southern Oscillation events are generally well captured with RMSEs ranging from 3.12 to 3.04 during El Niño years and from 3.62 to 2.44 during La Niña years. The forecast skill of the model for the past 10 years (2003–2012) is competitive with the current forecast schemes. The forecast model initialized in March, May, and June 2013 suggests an inactive season for 2013, with about 22 tropical cyclones.

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An assessment of the surface climate in the NCEP climate forecast system reanalysis

Cited by 154 publications

References 44 publications

Significant uncertainty in global scale hydrological modeling from precipitation data errors

Significant uncertainty in global scale hydrological modeling from precipitation data errors

Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter

A dynamical‐statistical forecast model for the annual frequency of western Pacific tropical cyclones based on the NCEP Climate Forecast System version 2

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